{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bfffc6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################################\n",
    "# MINpuT made in collaberation between the Arias and Kim groups at Cornell University\n",
    "# Drake Niedzielski, Eli Gerber, Yanjun Liu, Eun-Ah Kim, Tomas Arias\n",
    "# February 19 2024\n",
    "########################################################################################\n",
    "\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Angstrom = 0.529177 # This many Angstroms per bohr\n",
    "\n",
    "# return a list of all lines in filename with pattern\n",
    "def grep(pattern, filename):\n",
    "    retval = [] \n",
    "    foundPattern = False\n",
    "    file = open(filename, \"r\")\n",
    "    for line in file:\n",
    "        if re.search(pattern, line):\n",
    "            retval.append(line)\n",
    "            foundPattern = True\n",
    "    if not foundPattern:\n",
    "        print(\"grep failed to find:\", pattern, \"in\", filename)\n",
    "        print(\"returning an empty list\")\n",
    "    return retval\n",
    "\n",
    "# NEED remove comments\n",
    "def removeComments(stringList, commentChar):\n",
    "    stringListNoComments = []\n",
    "    for eachStr in stringList:\n",
    "        # remove leading whitespace and then partition on the comment character\n",
    "        partitionedLine = eachStr.lstrip().partition(commentChar) \n",
    "        #print(partitionedLine[0])\n",
    "        if partitionedLine[0] != \"\":\n",
    "            stringListNoComments.append(partitionedLine[0])\n",
    "    return stringListNoComments\n",
    " \n",
    "def readFor(pattern, filename):\n",
    "    stringList = removeComments( grep() )\n",
    "    if len(stringList) == 0:\n",
    "        # No command provided -> choosing default value\n",
    "        print()\n",
    "    if len(stringList) > 1:\n",
    "        print(\"WARNING! Multiple field entries found:\")\n",
    "        print(stringList)\n",
    "        print(\"Choosing the first one by default\")\n",
    "    return stringList[0]\n",
    "\n",
    "def readFor(pattern, filename):\n",
    "    return chooseFirst( removeComments( grep() ) )\n",
    "    \n",
    "# Generated using chatGPT 7/19/23\n",
    "# I haven't checked to see if it's 100% correct yet\n",
    "atomic_numbers = {\n",
    "    'H': 1,\n",
    "    'He': 2,\n",
    "    'Li': 3,\n",
    "    'Be': 4,\n",
    "    'B': 5,\n",
    "    'C': 6,\n",
    "    'N': 7,\n",
    "    'O': 8,\n",
    "    'F': 9,\n",
    "    'Ne': 10,\n",
    "    'Na': 11,\n",
    "    'Mg': 12,\n",
    "    'Al': 13,\n",
    "    'Si': 14,\n",
    "    'P': 15,\n",
    "    'S': 16,\n",
    "    'Cl': 17,\n",
    "    'Ar': 18,\n",
    "    'K': 19,\n",
    "    'Ca': 20,\n",
    "    'Sc': 21,\n",
    "    'Ti': 22,\n",
    "    'V': 23,\n",
    "    'Cr': 24,\n",
    "    'Mn': 25,\n",
    "    'Fe': 26,\n",
    "    'Ni': 28,\n",
    "    'Co': 27,\n",
    "    'Cu': 29,\n",
    "    'Zn': 30,\n",
    "    'Ga': 31,\n",
    "    'Ge': 32,\n",
    "    'As': 33,\n",
    "    'Se': 34,\n",
    "    'Br': 35,\n",
    "    'Kr': 36,\n",
    "    'Rb': 37,\n",
    "    'Sr': 38,\n",
    "    'Y': 39,\n",
    "    'Zr': 40,\n",
    "    'Nb': 41,\n",
    "    'Mo': 42,\n",
    "    'Tc': 43,\n",
    "    'Ru': 44,\n",
    "    'Rh': 45,\n",
    "    'Pd': 46,\n",
    "    'Ag': 47,\n",
    "    'Cd': 48,\n",
    "    'In': 49,\n",
    "    'Sn': 50,\n",
    "    'Sb': 51,\n",
    "    'I': 53,\n",
    "    'Te': 52,\n",
    "    'Xe': 54,\n",
    "    'Cs': 55,\n",
    "    'Ba': 56,\n",
    "    'La': 57,\n",
    "    'Ce': 58,\n",
    "    'Pr': 59,\n",
    "    'Nd': 60,\n",
    "    'Pm': 61,\n",
    "    'Sm': 62,\n",
    "    'Eu': 63,\n",
    "    'Gd': 64,\n",
    "    'Tb': 65,\n",
    "    'Dy': 66,\n",
    "    'Ho': 67,\n",
    "    'Er': 68,\n",
    "    'Tm': 69,\n",
    "    'Yb': 70,\n",
    "    'Lu': 71,\n",
    "    'Hf': 72,\n",
    "    'Ta': 73,\n",
    "    'W': 74,\n",
    "    'Re': 75,\n",
    "    'Os': 76,\n",
    "    'Ir': 77,\n",
    "    'Pt': 78,\n",
    "    'Au': 79,\n",
    "    'Hg': 80,\n",
    "    'Tl': 81,\n",
    "    'Pb': 82,\n",
    "    'Bi': 83,\n",
    "    'Po': 84,\n",
    "    'At': 85,\n",
    "    'Rn': 86,\n",
    "    'Fr': 87,\n",
    "    'Ra': 88,\n",
    "    'Ac': 89,\n",
    "    'Th': 90,\n",
    "    'Pa': 91,\n",
    "    'U': 92,\n",
    "    'Np': 93,\n",
    "    'Pu': 94,\n",
    "    'Am': 95,\n",
    "    'Cm': 96,\n",
    "    'Bk': 97,\n",
    "    'Cf': 98,\n",
    "    'Es': 99,\n",
    "    'Fm': 100,\n",
    "    'Md': 101,\n",
    "    'No': 102,\n",
    "    'Lr': 103,\n",
    "    'Rf': 104,\n",
    "    'Db': 105,\n",
    "    'Sg': 106,\n",
    "    'Bh': 107,\n",
    "    'Hs': 108,\n",
    "    'Mt': 109,\n",
    "    'Ds': 110,\n",
    "    'Rg': 111,\n",
    "    'Cn': 112,\n",
    "    'Nh': 113,\n",
    "    'Fl': 114,\n",
    "    'Mc': 115,\n",
    "    'Lv': 116,\n",
    "    'Ts': 117,\n",
    "    'Og': 118\n",
    "}        \n",
    "   \n",
    "####################################################################################\n",
    "# Functions for writing the lattice, ion positions, and ion species to output files\n",
    "# as of (PWscf, XCrysDen, CIF, VASP, Castep, and PDB)\n",
    "####################################################################################\n",
    "\n",
    "# Write to an ionpos file (JDFTx)\n",
    "def writeIonposFile(ionpos,ionsp,fname):\n",
    "    with open(fname,'w') as f:\n",
    "        f.write(\"#Ionic Positions in Lattice Coordinates \\n\")\n",
    "        for i in range(len(ionsp)):\n",
    "            f.write(\"ion  \"+ionsp[i]+\" \"+str(ionpos[i][0])+\" \"+str(ionpos[i][1])+\" \"+str(ionpos[i][2])+\" 1 \\n\")\n",
    "\n",
    "def writeLatticeFile(M, fname):\n",
    "    with open(fname,'w') as f:\n",
    "        f.write(\"lattice \\ \\n\")\n",
    "        for i in range(2):\n",
    "            f.write(str(M[i][0])+\"  \"+str(M[i][1])+\"  \"+str(M[i][2])+\" \\ \\n\")\n",
    "        f.write(str(M[2][0])+\"  \"+str(M[2][1])+\"  \"+str(M[2][2]))\n",
    "\n",
    "# fname acts as the file pattern here e.g. \"system.ionpos\" and \"system.lattice\"\n",
    "def writeJDFTx(M,ionpos,ionsp,path):\n",
    "    print(\"Writing to\", path+\"/system.ionpos\")\n",
    "    writeIonposFile(ionpos,ionsp,path+\"/system.ionpos\")\n",
    "    print(\"Writing to\", path+\"/system.lattice\")\n",
    "    writeLatticeFile(M, path+\"/system.lattice\")\n",
    "        \n",
    "def writeXSF(M,ionpos,ionsp,path):\n",
    "    M_Angstrom = M*Angstrom\n",
    "    \n",
    "    print(\"Writing to\", path+\"/system.xsf\")\n",
    "    with open(path+\"/system.xsf\",'w') as xsf_file:\n",
    "        xsf_file.write(\"CRYSTAL \\n\")\n",
    "        xsf_file.write(\"PRIMVEC \\n\")\n",
    "        for i in range(3):\n",
    "            xsf_file.write(\"  \"+str(M_Angstrom.T[i][0])+\"   \"+str(M_Angstrom.T[i][1])+\"   \"+str(M_Angstrom.T[i][2])+\" \\n\")\n",
    "        xsf_file.write(\"PRIMCOORD \\n\")\n",
    "        xsf_file.write(str(len(ionpos))+\"   1 \\n\")\n",
    "        ionpos_Angstrom = lat2cart(M_Angstrom,ionpos)\n",
    "        for i in range(len(ionpos)):\n",
    "            xsf_file.write(str(atomic_numbers[ionsp[i]])+\"  \"+str(ionpos_Angstrom[i][0])+\"  \"+str(ionpos_Angstrom[i][1])+\"  \"+str(ionpos_Angstrom[i][2]) + \" \\n\" )     \n",
    "            \n",
    "def writeCIF(M,ionpos,ionsp,path):\n",
    "    # Convert the lattice from bohr to angstrom\n",
    "    M_Angstrom = M*Angstrom\n",
    "    # Compute magnitudes and angles\n",
    "    a_vec, b_vec, c_vec = M_Angstrom[:,0], M_Angstrom[:,1], M_Angstrom[:,2]\n",
    "    a_mag, b_mag, c_mag = np.linalg.norm(a_vec), np.linalg.norm(b_vec), np.linalg.norm(c_vec)\n",
    "    alpha = np.arccos(np.dot(b_vec,c_vec)/(b_mag*c_mag))*180/np.pi # angle between b and c\n",
    "    beta  = np.arccos(np.dot(a_vec,c_vec)/(a_mag*c_mag))*180/np.pi # angle between a and c\n",
    "    gamma = np.arccos(np.dot(a_vec,b_vec)/(a_mag*b_mag))*180/np.pi # angle between a and b\n",
    "    volume = np.abs (np.dot(np.cross(a_vec,b_vec),c_vec)) # scalar triple product is the volume\n",
    "    \n",
    "    print(\"Writing to\", path+\"/system.cif\")\n",
    "    # Open the CIF file for writing\n",
    "    with open(path+\"/system.cif\",'w') as cif_file:\n",
    "        \n",
    "        # Write CIF header and lattice information\n",
    "        cif_file.write(\"data_generated_by_python\\n\")\n",
    "        cif_file.write(\"\\n\") # Spaces are important\n",
    "        cif_file.write(\"_cell_length_a {:f}\\n\".format(a_mag))\n",
    "        cif_file.write(\"_cell_length_b {:f}\\n\".format(b_mag))\n",
    "        cif_file.write(\"_cell_length_c {:f}\\n\".format(c_mag))\n",
    "        cif_file.write(\"_cell_angle_alpha {:f}\\n\".format(alpha)) # angle between b and c\n",
    "        cif_file.write(\"_cell_angle_beta {:f}\\n\".format(beta))   # angle between a and c\n",
    "        cif_file.write(\"_cell_angle_gamma {:f}\\n\".format(gamma)) # angle between a and b\n",
    "        cif_file.write(\"_cell_volume {:f}\\n\".format(volume))\n",
    "        cif_file.write(\"\\n\") # Spaces are important\n",
    "        \n",
    "        # Ignore symmetry information\n",
    "\n",
    "        # Write atomic coordinates loop header\n",
    "        cif_file.write(\"loop_\\n\")\n",
    "        cif_file.write(\"_atom_site_type_symbol\\n\")\n",
    "        cif_file.write(\"_atom_site_label\\n\")\n",
    "        cif_file.write(\"_atom_site_fract_x\\n\")\n",
    "        cif_file.write(\"_atom_site_fract_y\\n\")\n",
    "        cif_file.write(\"_atom_site_fract_z\\n\")\n",
    "\n",
    "        # Write atomic coordinates\n",
    "        for i in range(len(ionsp)):\n",
    "            cif_file.write(\"{}{} {} {:f} {:f} {:f}\\n\".format(ionsp[i], i, ionsp[i], ionpos[i][0], ionpos[i][1], ionpos[i][2] ))\n",
    "        \n",
    "        \n",
    "def writePOSCAR(M,ionpos,ionsp,path):\n",
    "    M_Angstrom = M.T*Angstrom # transpose for VASP convention \n",
    "    unique_ionsp, counts = np.unique(ionsp, return_counts=True)\n",
    "    chemical_name = \"\"\n",
    "    for i in range(len(unique_ionsp)):\n",
    "        chemical_name += unique_ionsp[i]+str(counts[i])\n",
    "        \n",
    "    print(\"Writing to\", path+\"/POSCAR\")\n",
    "    with open(path+\"/POSCAR\",'w') as poscar_file:\n",
    "        poscar_file.write(chemical_name+\"\\n\")\n",
    "        # Write Lattice information\n",
    "        poscar_file.write(\"1.00\\n\")\n",
    "        for i in range(3):\n",
    "            poscar_file.write(str(M_Angstrom[i][0])+\"  \"+str(M_Angstrom[i][1])+\"  \"+str(M_Angstrom[i][2])+\" \\n\")\n",
    "        # Write the ionic species information\n",
    "        for i in range(len(unique_ionsp)):\n",
    "            poscar_file.write(unique_ionsp[i]+\" \")\n",
    "        poscar_file.write(\"\\n\")\n",
    "        for i in range(len(counts)):\n",
    "            poscar_file.write(str(counts[i])+\" \")\n",
    "        poscar_file.write(\"\\n\")\n",
    "        # Write ionic position information in lattice coords\n",
    "        poscar_file.write(\"Direct\\n\")\n",
    "        for i in range(len(ionsp)):\n",
    "            poscar_file.write(\"{:f} {:f} {:f}\\n\".format(ionpos[i][0], ionpos[i][1], ionpos[i][2] ))\n",
    "            \n",
    "# This just writes the ATOMIC_POSITIONS and CELL_PARAMETERS fields\n",
    "# The rest is up to the user for now\n",
    "def writePWscf(M,ionpos,ionsp,path):\n",
    "    print(\"Writing to\", path+\"/pw.x\")\n",
    "    with open(path+\"/pw.x\",'w') as pwscf_file:\n",
    "        pwscf_file.write(\"CELL_PARAMETERS bohr\\n\")\n",
    "        for i in range(3):\n",
    "            pwscf_file.write(\"{:f} {:f} {:f}\\n\".format(M[0][i], M[1][i], M[2][i])) # transpose convention\n",
    "        pwscf_file.write(\"\\n\")\n",
    "        # Ionic position and species information\n",
    "        pwscf_file.write(\"ATOMIC_POSITIONS crystal\\n\")\n",
    "        for i in range(len(ionsp)):\n",
    "            pwscf_file.write(\"{} {:f} {:f} {:f}\\n\".format(ionsp[i], ionpos[i][0], ionpos[i][1], ionpos[i][2]))\n",
    "    \n",
    "# Use a dictionary to handle the different output cases\n",
    "# \"JDFTx\",\"XSF\",\"CIF\",\"POSCAR\",\"PWscf\"\n",
    "output_options = {\"JDFTx\" : writeJDFTx,\n",
    "                  \"XSF\" : writeXSF,\n",
    "                  \"CIF\" : writeCIF,\n",
    "                  \"POSCAR\" : writePOSCAR,\n",
    "                  \"PWscf\" : writePWscf\n",
    "                 }\n",
    "    \n",
    "#########################################\n",
    "# End section of output functions\n",
    "#########################################\n",
    "\n",
    "            \n",
    "# Create dictionary of default values\n",
    "inputParams = {\n",
    "    \"INPUT_FORMAT\": \"JDFTx\",\n",
    "    \"OUTPUT_FORMAT\": \"JDFTx\",\n",
    "    \"SubstrateLattice\": None,\n",
    "    \"SubstrateIonpos\": None,\n",
    "    \"FlakeLattice\": None,\n",
    "    \"FlakeIonpos\": None,\n",
    "    \"SubstrateCIF\": None,\n",
    "    \"FlakeCIF\": None,\n",
    "    \"FlakeTermination\": \"N\",\n",
    "    \"TermAtomDist\": [\"0.0\",\"bohr\"],\n",
    "    \"FlakeEdgeHPC\": \"None\",\n",
    "    \"FlakeXCOMHPC\": \"N\",\n",
    "    \"FlakeYCOMHPC\": \"N\",\n",
    "    \"FlakeRotaHPC\": \"N\",\n",
    "    \"FlakeSupercell\": None,\n",
    "    \"FlakeCut\": [],              # Flake Cut can be issued multiple times\n",
    "    \"FlakeRotate\": [\"0.0\", \"rad\", \"0.0\", \"0.0\", \"bohr\"],   # Rotate by 0 radians centered on the axis through 0.0, 0.0\n",
    "    \"FlakeShift\": [\"0.0\", \"0.0\", \"bohr\"],\n",
    "    \"MinVacuumPad\": [\"5.0\",\"5.0\",\"bohr\"],  # Padding between periodic images in the two different lattice directions\n",
    "                                   # Use 0.0 to indicate a commensurate lock-in\n",
    "    \"InterlayerDistance\": None,\n",
    "    \"LatticeVectorC\": None\n",
    "}\n",
    "\n",
    "allowedParamValues = {\n",
    "    \"INPUT_FORMAT\": [[\"JDFTx\",\"CIF\"]],\n",
    "    \"OUTPUT_FORMAT\": [[\"JDFTx\",\"XSF\",\"CIF\",\"POSCAR\",\"PWscf\"]],\n",
    "    \"SubstrateLattice\": \"ANY\",\n",
    "    \"SubstrateIonpos\": \"ANY\",\n",
    "    \"FlakeLattice\": \"ANY\",\n",
    "    \"FlakeIonpos\": \"ANY\",\n",
    "    \"SubstrateCIF\": \"ANY\",\n",
    "    \"FlakeCIF\": \"ANY\",\n",
    "    \"FlakeTermination\": [[\"Y\",\"N\"]],\n",
    "    \"TermAtomDist\": [[\"ANY\"],[\"bohr\",\"Angstrom\"]],\n",
    "    \"FlakeEdgeHPC\": \"None\",\n",
    "    \"FlakeXCOMHPC\": [[\"Y\",\"N\"]],\n",
    "    \"FlakeYCOMHPC\": [[\"Y\",\"N\"]],\n",
    "    \"FlakeRotaHPC\": [[\"Y\",\"N\"]],\n",
    "    \"FlakeSupercell\": [[\"ANY\"], [\"ANY\"]],\n",
    "    \"FlakeCut\": \"ANY\",\n",
    "    \"FlakeRotate\": [ [\"ANY\"], [\"rad\",\"deg\"], [\"ANY\"], [\"ANY\"], [\"bohr\",\"Angstrom\",\"latticeFlake\"]],   # Rotate by 0 radians centered on the axis through 0.0, 0.0\n",
    "    \"FlakeShift\": [[\"ANY\"], [\"ANY\"], [\"bohr\",\"Angstrom\",\"latticeFlake\",\"latticeSubstrate\"]],\n",
    "    \"MinVacuumPad\": [[\"ANY\"],[\"ANY\"], [\"bohr\",\"Angstrom\"] ], \n",
    "    \"InterlayerDistance\": [[\"ANY\"], [\"bohr\",\"Angstrom\"] ], \n",
    "    \"LatticeVectorC\": [[\"ANY\"],[\"ANY\"],[\"ANY\"],[\"bohr\",\"Angstrom\"]]\n",
    "}\n",
    "\n",
    "def isValidCommand(splitLine):\n",
    "    #print(splitLine)\n",
    "    key = splitLine[0]\n",
    "    APVs = allowedParamValues[key]\n",
    "    if APVs == \"ANY\":\n",
    "        return True\n",
    "    elif APVs == \"None\":\n",
    "        print(\"WARNING: I haven't implemented\",key,\"yet, so this isn't doing anything\")\n",
    "        return True\n",
    "    else:\n",
    "        for i in range(len(splitLine)-1):\n",
    "            if (not splitLine[i+1] in allowedParamValues[key][i]) and allowedParamValues[key][i][0] != \"ANY\":\n",
    "                return False\n",
    "    # Return True if it failed all of the invalidity checks   \n",
    "    return True\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb67ad51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FlakeCut 0.1 0.1  1  1 latticeFlake\\n', 'FlakeCut 0 1.333 0 -1 latticeFlake\\n', 'FlakeCut 7.937 6.35013 -1 -1 Angstrom\\n']\n",
      "{'INPUT_FORMAT': ['JDFTx'], 'OUTPUT_FORMAT': ['CIF'], 'SubstrateLattice': ['NbSe2.lattice'], 'SubstrateIonpos': ['NbSe2.ionpos'], 'FlakeLattice': ['NbSe2.lattice'], 'FlakeIonpos': ['NbSe2.ionpos'], 'SubstrateCIF': None, 'FlakeCIF': None, 'FlakeTermination': ['Y'], 'TermAtomDist': ['1.7', 'Angstrom'], 'FlakeEdgeHPC': 'None', 'FlakeXCOMHPC': 'N', 'FlakeYCOMHPC': 'N', 'FlakeRotaHPC': 'N', 'FlakeSupercell': ['3', '2'], 'FlakeCut': [['0.1', '0.1', '1', '1', 'latticeFlake'], ['0', '1.333', '0', '-1', 'latticeFlake'], ['7.937', '6.35013', '-1', '-1', 'Angstrom']], 'FlakeRotate': ['30', 'deg', '1.333333', '1.333333', 'latticeFlake'], 'FlakeShift': ['0.0', '0.0', 'latticeSubstrate'], 'MinVacuumPad': ['2.5', '2.5', 'Angstrom'], 'InterlayerDistance': ['2.5', 'Angstrom'], 'LatticeVectorC': ['0.0', '0.0', '18.0', 'Angstrom']}\n"
     ]
    }
   ],
   "source": [
    "#################################################################\n",
    "# User should edit inFileName to the name of the input file they constructed ( <filename>.in )\n",
    "inFileName = \"graphene_hBN.in\" #\n",
    "#################################################################\n",
    "\n",
    "    \n",
    "# Parse the input file:\n",
    "# Load entire input file into a list of strings\n",
    "with open(inFileName, \"r\") as inFile:\n",
    "    inFileLines = inFile.readlines()\n",
    "\n",
    "# Remove leading whitespace and comments\n",
    "inFileLines = removeComments(inFileLines,\"#\")\n",
    "\n",
    "#print(inFileLines)\n",
    "\n",
    "# For each parameter type\n",
    "toBeRemoved = []\n",
    "for key in inputParams:\n",
    "    # handle the multiple FlakeCut commands separately\n",
    "    if key != \"FlakeCut\":\n",
    "        # find the first line starting with \"key\"\n",
    "        for line in inFileLines:\n",
    "            splitLine = line.split()\n",
    "            if splitLine[0] == key:\n",
    "                # update from the default value if the command is valid\n",
    "                if isValidCommand(splitLine):\n",
    "                    inputParams[key] = splitLine[1:]\n",
    "                    # mark this line for removal from inFileLines\n",
    "                    #inFileLines.remove(line)\n",
    "                    toBeRemoved.append(line)\n",
    "                    break\n",
    "# Remove the found commands leaving the \"FlakeCut\"'s and unparsable lines\n",
    "for line in toBeRemoved:\n",
    "    inFileLines.remove(line)\n",
    "print(inFileLines)\n",
    "    \n",
    "# Do another pass to handle the \"FlakeCut\" commands\n",
    "toBeRemoved = []\n",
    "for line in inFileLines:\n",
    "    #print(\"line:\", line)\n",
    "    splitLine = line.split()\n",
    "    if splitLine[0] == \"FlakeCut\": \n",
    "        inputParams[\"FlakeCut\"].append(splitLine[1:])\n",
    "        #inFileLines.remove(line)\n",
    "        toBeRemoved.append(line)\n",
    "# Remove the \"FlakeCut\" commands leaving only the unparsable lines\n",
    "for line in toBeRemoved:\n",
    "    inFileLines.remove(line)\n",
    "    \n",
    "# If there are remaining lines, then there were lines that didn't parse correctly\n",
    "# Print those and exit\n",
    "if len(inFileLines) > 0:\n",
    "    print()\n",
    "    print(\"ERROR: The following lines were unable to be parsed\")\n",
    "    for line in inFileLines:\n",
    "        print(line)\n",
    "    quit()\n",
    "    print()\n",
    "\n",
    "print(inputParams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2894b614",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flake Supercell: 3.0 2.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cut = ['0.1', '0.1', '1', '1', 'latticeFlake']\n",
      "cut = ['0', '1.333', '0', '-1', 'latticeFlake']\n",
      "cut = ['7.937', '6.35013', '-1', '-1', 'Angstrom']\n",
      "cut = [19.84212, 0.0, -11.4559, 0.0, 'cartesian']\n",
      "cut = [0.0, 22.9118, 0.0, -6.61404, 'cartesian']\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGxCAYAAACqUFbqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA16UlEQVR4nO3deXhTdd7//9cppSlgGy1QmkIphRvFAoMsAkXZ9BYoNyiCI24sA6Iortzcal2+gDNDFXXGQRQUEUQUGQfZBlRwWL2tAkK9FZABLRShtZcgDcs0peT8/uDXDLFtutAsJ30+rutcFzn5nOSd5p3kxck5nximaZoCAACwiIhgFwAAAFAdhBcAAGAphBcAAGAphBcAAGAphBcAAGAphBcAAGAphBcAAGAphBcAAGAphBcAAGAphBegBhYuXCjDMMpdpkyZ4hnXqlUrjR07tkb3YRiGHnjggVqq2LdVq1bJMAw1btxYLper3DGvvPKK/uM//kNRUVEyDEMnTpzQjBkztGLFioDUOG3atAr/5hcu/fr1C0g9FSntjYMHD/r1fir622/atEmGYWjTpk1+vX8gmCKDXQBgZQsWLFC7du281iUmJgapmpqbP3++JOn48eNasWKFRo4c6XV9dna2HnroId19990aM2aMIiMjFRMToxkzZuiWW27RsGHD/F7j3XffrUGDBnku5+Xlafjw4XrwwQd1xx13eNbHxsb6vRZf/uu//ktZWVlyOBx+vZ+K/vZdunRRVlaWUlNT/Xr/QDARXoCL0KFDB3Xr1i3YZVyU/Px8rV27Vtddd50+//xzzZ8/v0x42b17tyRpwoQJ6t69u1/rOXfunEpKSmSz2bzWt2jRQi1atPBcLt2z0bJlS/Xs2fOi7/df//qXoqOjZRjGRd1O06ZN1bRp04uup6ZiY2Nr5e8BhDK+NgICqKioSP/93/+tq666Sna7XXFxcUpLS9PKlSsr3dY0TT355JOqX7++5s2b51m/dOlSpaWlqVGjRrrkkks0cOBA7dq1q8o1vf322yopKdGjjz6q4cOH6x//+IcOHTrkub5fv3666667JEk9evSQYRgaO3asDMPQ6dOn9fbbb5f7lU1+fr7uvfdetWjRQlFRUUpJSdH06dNVUlLiGXPw4EEZhqGZM2fqD3/4g1JSUmSz2bRx48Yq1/9rO3bs0I033qi4uDhFR0erc+fO+utf/+o1pvSrnXXr1mncuHFq2rSpGjZsKJfLpX79+qlDhw7KyspSr1691KBBA7Vq1UoLFiyQJK1Zs0ZdunRRw4YN1bFjR3388cfl3vaFXxuV3ub27dvVu3dvNWzYUK1bt9Zzzz0nt9vtGVfV/vD1t6/oa6NVq1YpLS1NDRs2VExMjG644QZlZWV5jSn9am737t26/fbbZbfb1axZM40bN06FhYU1eToAvyC8ABehdC/BhYsvLpdLx48f15QpU7RixQotWbJE1157rYYPH65Fixb53O6OO+7Q7NmztXr1ak2YMEHS+a8Obr/9dqWmpuqvf/2r3nnnHZ08eVK9e/fWnj17qvQY3nrrLTkcDqWnp2vcuHFyu91auHCh5/rXXntNTz/9tKTzX5NlZWXpmWeeUVZWlho0aKDBgwcrKytLWVlZeu211ySdDy7du3fXJ598ov/3//6fPvroI40fP16ZmZme2i80a9YsbdiwQS+++KI++uijMl/FVdXGjRt1zTXX6MSJE5o7d65Wrlypq666SiNHjvR6TKXGjRun+vXr65133tHf/vY31a9f31P/7373O919991auXKlOnbsqHHjxunZZ59VRkaGHnvsMS1btkyXXHKJhg0bpqNHj1ZaW35+vu68807dddddWrVqldLT05WRkaHFixd7xlS1P3z97cvz3nvv6aabblJsbKyWLFmi+fPn65dfflG/fv302WeflRk/YsQIXX755Vq2bJmeeOIJvffee3r00UcrfYxAwJgAqm3BggWmpHKXs2fPesYlJyebY8aMqfB2SkpKzLNnz5rjx483O3fu7HWdJHPSpEnmsWPHzGuvvdZs3ry5mZ2d7bk+NzfXjIyMNB988EGv7U6ePGkmJCSYt956a6WPY8uWLaYk84knnjBN0zTdbreZkpJiJicnm263u8zj3b59u9f2jRo1Kvfx3XvvveYll1xiHjp0yGv9iy++aEoyd+/ebZqmaebk5JiSzDZt2pjFxcWV1nuh0m1feOEFz7p27dqZnTt39noOTNM0hwwZYjocDvPcuXNej2f06NFlbrdv376mJHPHjh2edceOHTPr1atnNmjQwDxy5IhnfXZ2tinJnDVrlmdd6W3n5OSUuc0vv/zS675SU1PNgQMHVvgYffVHRX/7jRs3mpLMjRs3mqZpmufOnTMTExPNjh07eh6/aZ7vk/j4eLNXr16edVOnTjUlmTNnzvS6zfvvv9+Mjo726gkgmNjzAlyERYsWafv27V5LZKTvQ8k++OADXXPNNbrkkksUGRmp+vXra/78+dq7d2+ZsTk5OUpLS1NhYaG++OILderUyXPdJ598opKSEo0ePdprz090dLT69u1bpbNNSg/UHTdunCR5vhI6dOiQ/vGPf1TjL+Ht73//u/r376/ExESv2tLT0yVJmzdv9hp/4403evZ61NSBAwf03Xff6c4775Qkr/sdPHiw8vLytG/fPq9tRowYUe5tORwOde3a1XM5Li5O8fHxuuqqq7wOyL7yyislyetrtookJCSUOV7oN7/5TZltq9MfVbFv3z4dPXpUo0aNUkTEv9/yL7nkEo0YMUJffPGFzpw547XNjTfeWKbOoqIiFRQU1KgGoLYRXoCLcOWVV6pbt25eiy8ffvihbr31VjVv3lyLFy9WVlaWtm/frnHjxqmoqKjM+G3btumf//ynbrvtNq+DVSXpp59+kiRdffXVql+/vteydOlS/fzzzz5rOXnypD744AN1795dTZs21YkTJ3TixAndfPPNMgzDE2xq4qefftLq1avL1NW+fXtJKlNbbZyZU/r3mDJlSpn7vf/++6t1v3FxcWXWRUVFlVkfFRUlSeU+d7/WuHHjMutsNpv+9a9/eS5Xtz+q4tixY5LKf6yJiYlyu9365ZdffNZaevD0hbUCwcTZRkAALV68WCkpKVq6dKnXWS0Vza0ycuRIJSQk6KmnnpLb7fYceyJJTZo0kST97W9/U3JycrVrWbJkic6cOaNt27bpsssuK3P98uXL9csvv5R7XWWaNGmi3/zmN/rjH/9Y7vW/Pp38Ys/wKb1PScrIyNDw4cPLHXPFFVfU+v3Wpur2R1WUBpG8vLwy1x09elQRERE1eo6BYCK8AAFkGIZnkrdS+fn5Ps82evrppxUTE6NHH31Up0+fVmZmpiRp4MCBioyM1Pfff1/h1x++zJ8/XzExMVqxYoXX1wnS+TN2/ud//kfvvvuuz4nyfr3noNSQIUO0du1atWnTJmAfjFdccYXatm2rr7/+WjNmzAjIfda26vRHRX/7X7viiivUvHlzvffee5oyZYrntk+fPq1ly5Z5zkACrITwAgTQkCFD9OGHH+r+++/XLbfcosOHD+v3v/+9HA6H9u/fX+F2Dz/8sC655BLdc889OnXqlGbNmqVWrVrp2Wef1VNPPaUffvhBgwYN0mWXXaaffvpJ27ZtU6NGjTR9+vRyb+/bb7/Vtm3bdN999+m6664rc/0111yjl156SfPnz/cZXjp27KhNmzZp9erVcjgciomJ0RVXXKFnn31W69evV69evfTQQw/piiuuUFFRkQ4ePKi1a9dq7ty5Zb4Gqw2vv/660tPTNXDgQI0dO1bNmzfX8ePHtXfvXu3cuVMffPBBrd9nbapOf1T0t/+1iIgIzZw5U3feeaeGDBmie++9Vy6XSy+88IJOnDih5557LlAPD6g1hBcggH73u9+poKBAc+fO1VtvvaXWrVvriSee0I8//lhh0Cg1fvx4NWrUSKNGjdLp06f15ptvKiMjQ6mpqfrLX/6iJUuWyOVyKSEhQVdffbUmTpxY4W2VHs9y7733lnt9/fr1NXbsWD333HPauXNnhbfzl7/8RZMmTdJtt92mM2fOeA4Udjgc2rFjh37/+9/rhRde0I8//qiYmBilpKR4QpY/9O/fX9u2bdMf//hHPfLII/rll1/UuHFjpaam6tZbb/XLfdam6vRHRX/78txxxx1q1KiRMjMzNXLkSNWrV089e/bUxo0b1atXrwA8MqB2GaZpmsEuAgAAoKo42wgAAFgK4QUAAFgK4QUAAFgK4QUAAFgK4QUAAFgK4QUAAFhK2M3z4na7dfToUcXExITc1N8AAKB8pmnq5MmTSkxMLDPr96+FXXg5evSokpKSgl0GAACogcOHD1c6A3fYhZeYmBhJ5x98bGxskKsBAABV4XQ6lZSU5Pkc9yXswkvpV0WxsbGEFwAALKYqh3xwwC4AALAUwgsAALAUwgsAALAUwgsAALCUGoeXLVu2aOjQoUpMTJRhGFqxYoXX9YZhlLu88MILFd7mwoULy92mqKiopmUCAIAwU+Pwcvr0aXXq1EmzZ88u9/q8vDyv5a233pJhGBoxYoTP242NjS2zbXR0dE3LBAAAYabGp0qnp6crPT29wusTEhK8Lq9cuVL9+/dX69atfd6uYRhltgUAACgVkGNefvrpJ61Zs0bjx4+vdOypU6eUnJysFi1aaMiQIdq1a5fP8S6XS06n02sBAADhKyDh5e2331ZMTIyGDx/uc1y7du20cOFCrVq1SkuWLFF0dLSuueYa7d+/v8JtMjMzZbfbPQs/DQB/Oec2lfX9Ma3MPqKs74/pnNsMdkkIMHoACA2GaZoX/eozDEPLly/XsGHDyr2+Xbt2uuGGG/TKK69U63bdbre6dOmiPn36aNasWeWOcblccrlcnsul0wsXFhYywy5qzcff5mn66j3KK/z3weMOe7SmDk3VoA6OIFaGQKEHAP9yOp2y2+1V+vz2+56XrVu3at++fbr77rurvW1ERISuvvpqn3tebDab56cA+EkA+MPH3+bpvsU7vT60JCm/sEj3Ld6pj7/NC1JlCBR6AAgtfg8v8+fPV9euXdWpU6dqb2uaprKzs+Vw8L8aBMc5t6npq/eovN2Tpeumr97D1wdhjB4AQk+Nw8upU6eUnZ2t7OxsSVJOTo6ys7OVm5vrGeN0OvXBBx9UuNdl9OjRysjI8FyePn26PvnkE/3www/Kzs7W+PHjlZ2drYkTJ9a0TOCibMs5XuZ/2xcyJeUVFmlbzvHAFYWAogeA0FPjU6V37Nih/v37ey5PnjxZkjRmzBgtXLhQkvT+++/LNE3dfvvt5d5Gbm6uIiL+nZ9OnDihe+65R/n5+bLb7ercubO2bNmi7t2717RM4KIUnKzaBIlVHQfroQeA0FMrB+yGkuoc8ANUJuv7Y7p93heVjlsyoafS2jQOQEUINHoACIyQOmAXsLLuKXFy2KNlVHC9ofNnnHRPiQtkWQggegAIPYQXwId6EYamDk2VpDIfXqWXpw5NVb2Iij7aYHX0ABB6CC9AJQZ1cGjOXV2UYPf+ja0Ee7Tm3NWFOT7qAHoACC0c8wJU0Tm3qW05x1VwskjxMee/JuB/23ULPQD4T3U+v2t8thFQ19SLMDggs46jB4DQwNdGAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUggvAADAUmocXrZs2aKhQ4cqMTFRhmFoxYoVXtePHTtWhmF4LT179qz0dpctW6bU1FTZbDalpqZq+fLlNS0RAACEoRqHl9OnT6tTp06aPXt2hWMGDRqkvLw8z7J27Vqft5mVlaWRI0dq1KhR+vrrrzVq1Cjdeuut+vLLL2taJgAACDOGaZrmRd+IYWj58uUaNmyYZ93YsWN14sSJMntkfBk5cqScTqc++ugjz7pBgwbpsssu05IlS6p0G06nU3a7XYWFhYqNja3yfQMAgOCpzue3X4952bRpk+Lj43X55ZdrwoQJKigo8Dk+KytLAwYM8Fo3cOBAff755xVu43K55HQ6vRYAABC+/BZe0tPT9e6772rDhg166aWXtH37dl133XVyuVwVbpOfn69mzZp5rWvWrJny8/Mr3CYzM1N2u92zJCUl1dpjAAAAoSfSXzc8cuRIz787dOigbt26KTk5WWvWrNHw4cMr3M4wDK/LpmmWWXehjIwMTZ482XPZ6XQSYAAACGN+Cy+/5nA4lJycrP3791c4JiEhocxeloKCgjJ7Yy5ks9lks9lqrU4AABDaAjbPy7Fjx3T48GE5HI4Kx6SlpWn9+vVe69atW6devXr5uzwAAGARNd7zcurUKR04cMBzOScnR9nZ2YqLi1NcXJymTZumESNGyOFw6ODBg3ryySfVpEkT3XzzzZ5tRo8erebNmyszM1OS9PDDD6tPnz56/vnnddNNN2nlypX69NNP9dlnn13EQwQAAOGkxuFlx44d6t+/v+dy6XEnY8aM0Zw5c/TNN99o0aJFOnHihBwOh/r376+lS5cqJibGs01ubq4iIv6986dXr156//339fTTT+uZZ55RmzZttHTpUvXo0aOmZQIAgDBTK/O8hBLmeQEAwHpCZp4XAACA2kZ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlhIZ7AIAqyguceudrIM6dPyMkuMaalRaK0VFkv/rEnoACA2GaZpmsIuoTU6nU3a7XYWFhYqNjQ12OQgTmWv3aN7WHLkveLVEGNKE3inKGJwavMIQMPQA4F/V+fxmzwtQicy1e/T6lpwy692mPOv58Apv9AAQWtjfCfhQXOLWvK1lP7QuNG9rjopL3AGqCIFGDwChh/AC+PBO1kGvrwnK4zbPj0N4ogeA0EN4AXw4dPxMrY6D9dADQOghvAA+JMc1rNVxsB56AAg9hBfAh1FprRRh+B4TYZwfh/BEDwChh/AC+BAVGaEJvVN8jpnQO4W5PsIYPQCEHk6VBipRegosc3zUXfQAEFqYpA6oImZXBT0A+E91Pr8JLyGAN0QAqD28p1oT4cVC4YUpxwGg9vCeal38PIBFMOU4ANQe3lPrDvajBQlTjgNA7eE9tW4hvAQJU44DQO3hPbVuqXF42bJli4YOHarExEQZhqEVK1Z4rjt79qwef/xxdezYUY0aNVJiYqJGjx6to0eP+rzNhQsXyjCMMktRUVFNywxZTDkOALWH99S6pcbh5fTp0+rUqZNmz55d5rozZ85o586deuaZZ7Rz5059+OGH+uc//6kbb7yx0tuNjY1VXl6e1xIdHV3TMkMWU44DQO3hPbVuqfEBu+np6UpPTy/3OrvdrvXr13ute+WVV9S9e3fl5uaqZcuWFd6uYRhKSEioaVmWMSqtlf64dq/P3ZxMOQ4AVcN7at0SsGNeCgsLZRiGLr30Up/jTp06peTkZLVo0UJDhgzRrl27fI53uVxyOp1eixUw5TgA1B7eU+uWgDyLRUVFeuKJJ3THHXf4PHe7Xbt2WrhwoVatWqUlS5YoOjpa11xzjfbv31/hNpmZmbLb7Z4lKSnJHw/BLzIGp+rePillfvQtwpDu7cOcBABQHbyn1h21MkmdYRhavny5hg0bVua6s2fP6re//a1yc3O1adOmak0c53a71aVLF/Xp00ezZs0qd4zL5ZLL5fJcdjqdSkpKsswkdRKzQQJAbeI91ZpCZpK6s2fP6tZbb1VOTo42bNhQ7TARERGhq6++2ueeF5vNJpvNdrGlBlVUZITG924d7DIAICzwnhr+/BZFS4PL/v379emnn6px48bVvg3TNJWdnS2Hw+GHCgEAgBXVeM/LqVOndODAAc/lnJwcZWdnKy4uTomJibrlllu0c+dO/f3vf9e5c+eUn58vSYqLi1NUVJQkafTo0WrevLkyMzMlSdOnT1fPnj3Vtm1bOZ1OzZo1S9nZ2Xr11Vcv5jECAIAwUuPwsmPHDvXv399zefLkyZKkMWPGaNq0aVq1apUk6aqrrvLabuPGjerXr58kKTc3VxER/975c+LECd1zzz3Kz8+X3W5X586dtWXLFnXv3r2mZQIAgDDDr0oDAICgq87nN4dfAwAASyG8AAAASyG8AAAASyG8AAAASyG8AAAAS/HrDLsIH+fcprblHFfBySLFx0Sre0qc6v36B0QQ1ugB0AMIFYQXVOrjb/M0ffUe5RUWedY57NGaOjRVgzow+3FdQA+AHkAo4Wsj+PTxt3m6b/FOrzcsScovLNJ9i3fq42/zglQZAoUeAD2AUEN4QYXOuU1NX71H5c1iWLpu+uo9OucOq3kOcQF6APQAQhHhBRXalnO8zP+0LmRKyiss0rac44ErCgFFD4AeQCgivKBCBScrfsOqyThYDz0AegChiPCCCsXHRNfqOFgPPQB6AKGI8IIKdU+Jk8MerYpOhDR0/myD7ilxgSwLAUQPgB5AKCK8oEL1IgxNHZoqSWXeuEovTx2ayjwPYYweAD2AUER4gU+DOjg0564uSrB77xJOsEdrzl1dmN+hDqAHQA8g1BimaYbV+W1Op1N2u12FhYWKjY0Ndjlhg5k1QQ+AHoA/Vefzmxl2USX1IgyltWkc7DIQRPQA6AGECr42AgAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAlkJ4AQAAllLj8LJlyxYNHTpUiYmJMgxDK1as8LreNE1NmzZNiYmJatCggfr166fdu3dXervLli1TamqqbDabUlNTtXz58pqWCAAAwlCNw8vp06fVqVMnzZ49u9zrZ86cqT/96U+aPXu2tm/froSEBN1www06efJkhbeZlZWlkSNHatSoUfr66681atQo3Xrrrfryyy9rWiYAAAgzhmma5kXfiGFo+fLlGjZsmKTze10SExP1yCOP6PHHH5ckuVwuNWvWTM8//7zuvffecm9n5MiRcjqd+uijjzzrBg0apMsuu0xLliypUi1Op1N2u12FhYWKjY29uAcGAAACojqf33455iUnJ0f5+fkaMGCAZ53NZlPfvn31+eefV7hdVlaW1zaSNHDgQJ/buFwuOZ1OrwUAAIQvv4SX/Px8SVKzZs281jdr1sxzXUXbVXebzMxM2e12z5KUlHQRlQMAgFDn17ONDMPwumyaZpl1F7tNRkaGCgsLPcvhw4drXjAAAAh5kf640YSEBEnn96Q4HA7P+oKCgjJ7Vn693a/3slS2jc1mk81mu8iKAQCAVfhlz0tKSooSEhK0fv16z7ri4mJt3rxZvXr1qnC7tLQ0r20kad26dT63AQAAdUuN97ycOnVKBw4c8FzOyclRdna24uLi1LJlSz3yyCOaMWOG2rZtq7Zt22rGjBlq2LCh7rjjDs82o0ePVvPmzZWZmSlJevjhh9WnTx89//zzuummm7Ry5Up9+umn+uyzzy7iIQIAgHBS4/CyY8cO9e/f33N58uTJkqQxY8Zo4cKFeuyxx/Svf/1L999/v3755Rf16NFD69atU0xMjGeb3NxcRUT8e+dPr1699P777+vpp5/WM888ozZt2mjp0qXq0aNHTcsEAABhplbmeQklzPMCAID1BH2eFwAAAH8hvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEuJDHYBsIbiErfeyTqoQ8fPKDmuoUaltVJUJNm3LqEHQA8gVBimaZrBLqI2OZ1O2e12FRYWKjY2NtjlhIXMtXs0b2uO3Bd0SoQhTeidoozBqcErDAFDD4AegL9V5/ObPS/wKXPtHr2+JafMercpz3reuMIbPQB6AKHGr/v7WrVqJcMwyiyTJk0qd/ymTZvKHf/dd9/5s0xUoLjErXlby75hXWje1hwVl7gDVBECjR4APYBQ5Nfwsn37duXl5XmW9evXS5J++9vf+txu3759Xtu1bdvWn2WiAu9kHfTaRVwet3l+HMITPQB6AKHIr18bNW3a1Ovyc889pzZt2qhv374+t4uPj9ell17qx8pQFYeOn6nVcbAeegD0AEJRwA4TLy4u1uLFizVu3DgZhuFzbOfOneVwOHT99ddr48aNPse6XC45nU6vBbUjOa5hrY6D9dADoAcQigIWXlasWKETJ05o7NixFY5xOBx64403tGzZMn344Ye64oordP3112vLli0VbpOZmSm73e5ZkpKS/FB93TQqrZUifOdMRRjnxyE80QOgBxCKAnaq9MCBAxUVFaXVq1dXa7uhQ4fKMAytWrWq3OtdLpdcLpfnstPpVFJSEqdK15KKzjIodW8fTpMMd/QA6AEEQsidKn3o0CF9+umn+vDDD6u9bc+ePbV48eIKr7fZbLLZbBdTHnwofUNifoe6ix4APYBQE5A9L9OmTdPrr7+uw4cPKzKyennplltu0fHjx7Vhw4YqjWeSOv9gZk3QA6AH4E8htefF7XZrwYIFGjNmTJngkpGRoSNHjmjRokWSpJdfflmtWrVS+/btPQf4Llu2TMuWLfN3mahEVGSExvduHewyEET0AOgBa6gLIdPv4eXTTz9Vbm6uxo0bV+a6vLw85ebmei4XFxdrypQpOnLkiBo0aKD27dtrzZo1Gjx4sL/LBADA8sr7GYc/rt0bdl/v8dtGAACEAasfWF2dz+/w2o8EAEAdVNd+xoHwAgCAxdW1n3EgvAAAYHF17WccCC8AAFhcXfsZB8ILAAAWV9d+xoHwAgCAxUVFRmhC7xSfYyb0Tgmb+V4C8vMAAADAv+rSzzgwzwsAAGHEqjPshtTPAwAAgMCpCz/jEPpRDAAA4AKEFwAAYCmEFwAAYCmEFwAAYCmEFwAAYCmEFwAAYCmEFwAAYCmEFwAAYCmEFwAAYCnMsFtF59ymtuUcV8HJIsXHRKt7SpzqVfYTnggr9ADoAdADoYHwUgUff5un6av3KK+wyLPOYY/W1KGpGtTBEcTKECj0AOgB0AOhgx9mrMTH3+bpvsU79es/UmnOnnNXF5o2zNEDoAdAD/hfdT6/OebFh3NuU9NX7ynTrJI866av3qNz7rDKf7gAPQB6APRA6CG8+LAt57jX7sFfMyXlFRZpW87xwBWFgKIHQA+AHgg9hBcfCk5W3Kw1GQfroQdAD4AeCD2EFx/iY6JrdRyshx4APQB6IPQQXnzonhInhz1aFZ0EZ+j8kebdU+ICWRYCiB4APQB6IPQQXnyoF2Fo6tBUSSrTtKWXpw5N5Rz/MEYPgB4APRB6CC+VGNTBoTl3dVGC3Xt3YII9mlPj6gh6APQA6IHQwjwvVcSsiqAHQA+AHvCf6nx+E14AAEDQMUkdAAAIW4QXAABgKX4NL9OmTZNhGF5LQkKCz202b96srl27Kjo6Wq1bt9bcuXP9WSIAALAYv/+qdPv27fXpp596LterV6/CsTk5ORo8eLAmTJigxYsX63//9391//33q2nTphoxYoS/SwUAABbg9/ASGRlZ6d6WUnPnzlXLli318ssvS5KuvPJK7dixQy+++CLhBQAASArAMS/79+9XYmKiUlJSdNttt+mHH36ocGxWVpYGDBjgtW7gwIHasWOHzp49W+42LpdLTqfTawEAAOHLr+GlR48eWrRokT755BPNmzdP+fn56tWrl44dO1bu+Pz8fDVr1sxrXbNmzVRSUqKff/653G0yMzNlt9s9S1JSUq0/DgAAEDr8Gl7S09M1YsQIdezYUf/5n/+pNWvWSJLefvvtCrcxDO/Jfkqnofn1+lIZGRkqLCz0LIcPH66l6gEAQCjy+zEvF2rUqJE6duyo/fv3l3t9QkKC8vPzvdYVFBQoMjJSjRs3Lncbm80mm81W67UCAIDQFNB5Xlwul/bu3SuHo/zfgEhLS9P69eu91q1bt07dunVT/fr1A1EiAAAIcX4NL1OmTNHmzZuVk5OjL7/8UrfccoucTqfGjBkj6fxXPqNHj/aMnzhxog4dOqTJkydr7969euuttzR//nxNmTLFn2UCAAAL8evXRj/++KNuv/12/fzzz2ratKl69uypL774QsnJyZKkvLw85ebmesanpKRo7dq1evTRR/Xqq68qMTFRs2bN4jRpAADgwQ8zAgCAoOOHGQEAQNgivAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEshvAAAAEvxa3jJzMzU1VdfrZiYGMXHx2vYsGHat2+fz202bdokwzDKLN99950/SwUAABbh1/CyefNmTZo0SV988YXWr1+vkpISDRgwQKdPn65023379ikvL8+ztG3b1p+lAgAAi4j0541//PHHXpcXLFig+Ph4ffXVV+rTp4/PbePj43XppZdWeh8ul0sul8tz2el01qhWAABgDQE95qWwsFCSFBcXV+nYzp07y+Fw6Prrr9fGjRsrHJeZmSm73e5ZkpKSaq1eAAAQegzTNM1A3JFpmrrpppv0yy+/aOvWrRWO27dvn7Zs2aKuXbvK5XLpnXfe0dy5c7Vp06Zy99aUt+clKSlJhYWFio2N9ctjAQAAtcvpdMput1fp8ztg4WXSpElas2aNPvvsM7Vo0aJa2w4dOlSGYWjVqlWVjq3OgwcAAKGhOp/fAfna6MEHH9SqVau0cePGagcXSerZs6f279/vh8oAAIDV+PWAXdM09eCDD2r58uXatGmTUlJSanQ7u3btksPhqOXqAACAFfk1vEyaNEnvvfeeVq5cqZiYGOXn50uS7Ha7GjRoIEnKyMjQkSNHtGjRIknSyy+/rFatWql9+/YqLi7W4sWLtWzZMi1btsyfpQIAAIvwa3iZM2eOJKlfv35e6xcsWKCxY8dKkvLy8pSbm+u5rri4WFOmTNGRI0fUoEEDtW/fXmvWrNHgwYP9WSpQqeISt97JOqhDx88oOa6hRqW1UlQkk1TXJfQA6IHQELADdgOFA3bhD5lr92je1hy5L3i1RBjShN4pyhicGrzCEDD0AOgB/6rO57df97wA4SBz7R69viWnzHq3Kc963rjCGz0AeiC0sK8L8KG4xK15W8u+YV1o3tYcFZe4A1QRAo0eAD0QeggvgA/vZB302kVcHrd5fhzCEz0AeiD0EF4AHw4dP1Or42A99ADogdBDeAF8SI5rWKvjYD30AOiB0EN4AXwYldZKEYbvMRHG+XEIT/QA6IHQQ3gBfIiKjNCE3r5nhp7QO4V5HsIYPQB6IPRwqjRQidLTH5nfoe6iB0APhBYmqQOqiJk1QQ+AHvCf6nx+E14AAEDQVefzm7gIAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAshfACAAAsJTLYBcAazrlNbcs5roKTRYqPiVb3lDjVizCCXRYCiB4APYBQ6YGAhJfXXntNL7zwgvLy8tS+fXu9/PLL6t27d4XjN2/erMmTJ2v37t1KTEzUY489pokTJwaiVJTj42/zNH31HuUVFnnWOezRmjo0VYM6OIJYGQKFHgA9gFDqAb9/bbR06VI98sgjeuqpp7Rr1y717t1b6enpys3NLXd8Tk6OBg8erN69e2vXrl168skn9dBDD2nZsmX+LhXl+PjbPN23eKdXs0pSfmGR7lu8Ux9/mxekyhAo9ADoAYRaDximaZr+vIMePXqoS5cumjNnjmfdlVdeqWHDhikzM7PM+Mcff1yrVq3S3r17PesmTpyor7/+WllZWZXen9PplN1uV2FhoWJjY2vnQdRR59ymrn1+Q5lmLWVISrBH67PHr2PXcZiiB0APIFA9UJ3Pb7/ueSkuLtZXX32lAQMGeK0fMGCAPv/883K3ycrKKjN+4MCB2rFjh86ePVtmvMvlktPp9FpQO7blHK+wWSXJlJRXWKRtOccDVxQCih4APYBQ7AG/hpeff/5Z586dU7NmzbzWN2vWTPn5+eVuk5+fX+74kpIS/fzzz2XGZ2Zmym63e5akpKTaewB1XMHJipu1JuNgPfQA6AGEYg8E5FRpw/DejWSaZpl1lY0vb70kZWRkqLCw0LMcPny4FiqGJMXHRNfqOFgPPQB6AKHYA34NL02aNFG9evXK7GUpKCgos3elVEJCQrnjIyMj1bhx4zLjbTabYmNjvRbUju4pcXLYo1VRzDR0/kjz7ilxgSwLAUQPgB5AKPaAX8NLVFSUunbtqvXr13utX79+vXr16lXuNmlpaWXGr1u3Tt26dVP9+vX9VivKqhdhaOrQVEkq07Sll6cOTeUgvTBGD4AeQCj2gN+/Npo8ebLefPNNvfXWW9q7d68effRR5ebmeuZtycjI0OjRoz3jJ06cqEOHDmny5Mnau3ev3nrrLc2fP19Tpkzxd6kox6AODs25q4sS7N67AxPs0ZpzVxfmd6gD6AHQAwi1HvD7qdLS+UnqZs6cqby8PHXo0EF//vOf1adPH0nS2LFjdfDgQW3atMkzfvPmzXr00Uc9k9Q9/vjjVZ6kjlOl/SNUZlVE8NADoAfgzx6ozud3QMJLIBFeAACwnpCZ5wUAAKC2EV4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClRAa7AADWUFzi1jtZB3Xo+BklxzXUqLRWiork/z91CT2AUGGYpmkGu4ja5HQ6ZbfbVVhYqNjY2GCXA4SFzLV7NG9rjtwXvFtEGNKE3inKGJwavMIQMPQA/K06n99+i8wHDx7U+PHjlZKSogYNGqhNmzaaOnWqiouLfW43duxYGYbhtfTs2dNfZQKoRObaPXp9i/eHliS5Ten1LTnKXLsnOIUhYOgBhBq/hZfvvvtObrdbr7/+unbv3q0///nPmjt3rp588slKtx00aJDy8vI8y9q1a/1VJgAfikvcmrc1x+eYeVtzVFziDlBFCDR6AKHIb8e8DBo0SIMGDfJcbt26tfbt26c5c+boxRdf9LmtzWZTQkKCv0oDUEXvZB0s87/tX3Ob58eN7906MEUhoOgBhKKAHmlVWFiouLi4Ssdt2rRJ8fHxuvzyyzVhwgQVFBRUONblcsnpdHotAGrHoeNnanUcrIceQCgKWHj5/vvv9corr2jixIk+x6Wnp+vdd9/Vhg0b9NJLL2n79u267rrr5HK5yh2fmZkpu93uWZKSkvxRPlAnJcc1rNVxsB56AKGo2mcbTZs2TdOnT/c5Zvv27erWrZvn8tGjR9W3b1/17dtXb775ZrUKzMvLU3Jyst5//30NHz68zPUul8sr2DidTiUlJXG2EVALikvcavfMRz6/NogwpO9+n84ps2GKHkCgVOdso2of8/LAAw/otttu8zmmVatWnn8fPXpU/fv3V1pamt54443q3p0cDoeSk5O1f//+cq+32Wyy2WzVvl0AlYuKjNCE3il6fUvFB2xO6J3Ch1YYowcQiqodXpo0aaImTZpUaeyRI0fUv39/de3aVQsWLFBERPWb+9ixYzp8+LAcDke1twVw8Urn8GCOj7qLHkCo8dskdaVfFbVs2VKLFi1SvXr1PNddeCZRu3btlJmZqZtvvlmnTp3StGnTNGLECDkcDh08eFBPPvmkcnNztXfvXsXExFR6v0xSB/gHs6uCHoA/+fVro6pat26dDhw4oAMHDqhFixZe112Yl/bt26fCwkJJUr169fTNN99o0aJFOnHihBwOh/r376+lS5dWKbgA8J+oyAhOha3j6AGECn4eAAAABF1I/DwAAACAPxBeAACApRBeAACApRBeAACApRBeAACApRBeAACApRBeAACApRBeAACApfhtht1gKZ1zz+l0BrkSAABQVaWf21WZOzfswsvJkyclSUlJSUGuBAAAVNfJkydlt9t9jgm7nwdwu906evSoYmJiZBhGsMsJK06nU0lJSTp8+DA/vRBEPA/Bx3MQGngegq82nwPTNHXy5EklJiYqIsL3US1ht+clIiKizA9BonbFxsbyRhECeB6Cj+cgNPA8BF9tPQeV7XEpxQG7AADAUggvAADAUggvqDKbzaapU6fKZrMFu5Q6jech+HgOQgPPQ/AF6zkIuwN2AQBAeGPPCwAAsBTCCwAAsBTCCwAAsBTCCwAAsBTCCwAAsBTCC6rstddeU0pKiqKjo9W1a1dt3bo12CXVGdOmTZNhGF5LQkJCsMsKe1u2bNHQoUOVmJgowzC0YsUKr+tN09S0adOUmJioBg0aqF+/ftq9e3dwig1TlT0HY8eOLfPa6NmzZ3CKDVOZmZm6+uqrFRMTo/j4eA0bNkz79u3zGhPo1wLhBVWydOlSPfLII3rqqae0a9cu9e7dW+np6crNzQ12aXVG+/btlZeX51m++eabYJcU9k6fPq1OnTpp9uzZ5V4/c+ZM/elPf9Ls2bO1fft2JSQk6IYbbvD8QCwuXmXPgSQNGjTI67Wxdu3aAFYY/jZv3qxJkybpiy++0Pr161VSUqIBAwbo9OnTnjEBfy2YQBV0797dnDhxote6du3amU888USQKqpbpk6danbq1CnYZdRpkszly5d7LrvdbjMhIcF87rnnPOuKiopMu91uzp07NwgVhr9fPwemaZpjxowxb7rppqDUU1cVFBSYkszNmzebphmc1wJ7XlCp4uJiffXVVxowYIDX+gEDBujzzz8PUlV1z/79+5WYmKiUlBTddttt+uGHH4JdUp2Wk5Oj/Px8r9eFzWZT3759eV0E2KZNmxQfH6/LL79cEyZMUEFBQbBLCmuFhYWSpLi4OEnBeS0QXlCpn3/+WefOnVOzZs281jdr1kz5+flBqqpu6dGjhxYtWqRPPvlE8+bNU35+vnr16qVjx44Fu7Q6q7T3eV0EV3p6ut59911t2LBBL730krZv367rrrtOLpcr2KWFJdM0NXnyZF177bXq0KGDpOC8FiL9cqsIS4ZheF02TbPMOvhHenq6598dO3ZUWlqa2rRpo7fffluTJ08OYmXgdRFcI0eO9Py7Q4cO6tatm5KTk7VmzRoNHz48iJWFpwceeED/93//p88++6zMdYF8LbDnBZVq0qSJ6tWrVyZBFxQUlEnaCIxGjRqpY8eO2r9/f7BLqbNKz/bidRFaHA6HkpOTeW34wYMPPqhVq1Zp48aNatGihWd9MF4LhBdUKioqSl27dtX69eu91q9fv169evUKUlV1m8vl0t69e+VwOIJdSp2VkpKihIQEr9dFcXGxNm/ezOsiiI4dO6bDhw/z2qhFpmnqgQce0IcffqgNGzYoJSXF6/pgvBb42ghVMnnyZI0aNUrdunVTWlqa3njjDeXm5mrixInBLq1OmDJlioYOHaqWLVuqoKBAf/jDH+R0OjVmzJhglxbWTp06pQMHDngu5+TkKDs7W3FxcWrZsqUeeeQRzZgxQ23btlXbtm01Y8YMNWzYUHfccUcQqw4vvp6DuLg4TZs2TSNGjJDD4dDBgwf15JNPqkmTJrr55puDWHV4mTRpkt577z2tXLlSMTExnj0sdrtdDRo0kGEYgX8t+OUcJoSlV1991UxOTjajoqLMLl26eE6Tg/+NHDnSdDgcZv369c3ExERz+PDh5u7du4NdVtjbuHGjKanMMmbMGNM0z58iOnXqVDMhIcG02Wxmnz59zG+++Sa4RYcZX8/BmTNnzAEDBphNmzY169evb7Zs2dIcM2aMmZubG+yyw0p5f39J5oIFCzxjAv1aMP7/wgAAACyBY14AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAIClEF4AAICl/H+uRUBPJoPbDAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making a 5 x 3 supercell of the substrate\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing to ./system.cif\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "#import time\n",
    "\n",
    "# These reading functions will be specific to DFTCODE_INPUT ##########################\n",
    "def get_ionpos_ionsp(path2file):\n",
    "    ionspecies = []\n",
    "    ionpos = []\n",
    "    with open(path2file,'r') as file:\n",
    "        # Skip the comment line at the top of the file\n",
    "        line = file.readline()\n",
    "        # record ionspecies, and lattice coordinate\n",
    "        while line:\n",
    "            line = file.readline()\n",
    "            line_text = line.split()\n",
    "            if len(line_text) > 0:\n",
    "                if line_text[0] == \"ion\":\n",
    "                    ionspecies.append(line_text[1])\n",
    "                    ionpos.append([float(x) for x in line_text[2:5]])\n",
    "    return ionpos,ionspecies  \n",
    "\n",
    "def get_LatticeMatrix(path2file):\n",
    "    LatticeMatrix = []\n",
    "    with open(path2file,'r') as file:\n",
    "        # skip the first two lines\n",
    "        #line = file.readline()\n",
    "        line = file.readline()\n",
    "        # get the contents as a list of lines\n",
    "        contents = file.readlines()\n",
    "        LM = [line.split()[:3] for line in contents]\n",
    "        LatticeMatrix = np.array([ [float(i) for i in lm] for lm in LM ])\n",
    "    return LatticeMatrix\n",
    "\n",
    "# TODO: This only works for nonsymmetrized fractional coordinate cif files\n",
    "# This does NOT support _atom_site_Cartn_(x,y,z)\n",
    "# ionspecies labels are from the \"_atom_site_label\" field\n",
    "def readCIF_aux(path2file):\n",
    "    # Read in the lattice parameters\n",
    "    a = float( grep(\"_cell_length_a\", path2file)[0].split()[1] )\n",
    "    b = float( grep(\"_cell_length_b\", path2file)[0].split()[1] )\n",
    "    c = float( grep(\"_cell_length_c\", path2file)[0].split()[1] )\n",
    "    alpha = float(grep(\"_cell_angle_alpha\", path2file)[0].split()[1])*np.pi/180\n",
    "    beta = float(grep(\"_cell_angle_beta\", path2file)[0].split()[1])*np.pi/180\n",
    "    gamma = float(grep(\"_cell_angle_gamma\", path2file)[0].split()[1])*np.pi/180\n",
    "    \n",
    "    # Form the lattice matrix s.t. a and b lie along the x-y plane\n",
    "    a_vec = a*np.array([1,0,0])\n",
    "    b_vec = b*np.array([np.cos(gamma),np.sin(gamma),0])\n",
    "    cx = np.cos(beta)\n",
    "    cy = (np.cos(alpha) - np.cos(beta)*np.cos(gamma) )/ np.sin(gamma)\n",
    "    cz = np.sqrt(1-cx**2-cy**2)\n",
    "    c_vec = c*np.array([cx,cy,cz])\n",
    "    LatticeMatrix = (1.0/Angstrom)*np.array([a_vec,b_vec,c_vec]).T\n",
    "    \n",
    "    # Parse the file and locate the loop containing the atomic coordinate information\n",
    "    ionpos = []\n",
    "    ionsp = []\n",
    "    with open(path2file,'r') as file:\n",
    "        prevline = \"\"\n",
    "        line = \"\"\n",
    "        for line in file:            \n",
    "            if prevline.strip() == \"loop_\" and \"_atom_site\" in line:\n",
    "                # Put all the \"_atom_site_*\" commands into a list\n",
    "                cmd_list = []\n",
    "                while \"_atom_site\" in line:\n",
    "                    cmd_list.append(line.strip()) # remove leading and trailing whitespace\n",
    "                    prevline = line\n",
    "                    line = file.readline()\n",
    "                # \n",
    "                site_label_index = cmd_list.index(\"_atom_site_label\")\n",
    "                site_atom_x_index = cmd_list.index(\"_atom_site_fract_x\")\n",
    "                site_atom_y_index = cmd_list.index(\"_atom_site_fract_y\")\n",
    "                site_atom_z_index = cmd_list.index(\"_atom_site_fract_z\")\n",
    "                \n",
    "                # Atom information listed until a whitespace\n",
    "                while len(line.split()) > 1:\n",
    "                    # make sure to remove all numbers from the label\n",
    "                    split = line.split()\n",
    "                    ionsp.append( ''.join([j for j in split[site_label_index] if not j.isdigit()]) ) \n",
    "                    ionpos.append( [float(split[site_atom_x_index]),float(split[site_atom_y_index]),float(split[site_atom_z_index])] )\n",
    "                    prevline = line\n",
    "                    line = file.readline()\n",
    "                    \n",
    "                break\n",
    "            #\n",
    "            prevline = line\n",
    "\n",
    "    return ionpos, ionsp, LatticeMatrix\n",
    "\n",
    "# returns ionpos, ionsp, and lattice information from the specified flake and substrate ionpos and lattice files\n",
    "def readJDFTx():\n",
    "    flakeIonpos, flakeIonsp = get_ionpos_ionsp(*inputParams[\"FlakeIonpos\"])\n",
    "    flakeLattice = get_LatticeMatrix(*inputParams[\"FlakeLattice\"])\n",
    "    subsIonpos, subsIonsp = get_ionpos_ionsp(*inputParams[\"SubstrateIonpos\"])\n",
    "    subsLattice = get_LatticeMatrix(*inputParams[\"SubstrateLattice\"])\n",
    "    return flakeIonpos, flakeIonsp, flakeLattice, subsIonpos, subsIonsp, subsLattice\n",
    "\n",
    "# returns ionpos, ionsp, and lattice information from the specified flake and substrate cif files\n",
    "def readCIF():\n",
    "    flakeIonpos, flakeIonsp, flakeLattice = readCIF_aux(*inputParams[\"FlakeCIF\"])\n",
    "    subsIonpos, subsIonsp, subsLattice = readCIF_aux(*inputParams[\"SubstrateCIF\"]) \n",
    "    return flakeIonpos, flakeIonsp, flakeLattice, subsIonpos, subsIonsp, subsLattice\n",
    "\n",
    "# Use a dictionary to handle the different output cases\n",
    "# \"JDFTx\",\"XSF\",\"CIF\",\"POSCAR\",\"PWscf\"\n",
    "input_options = {\"JDFTx\" : readJDFTx,\n",
    "                  \"CIF\" : readCIF,\n",
    "                 }\n",
    "\n",
    "######################################################################################\n",
    "\n",
    "def lat2cart(M,ionpos):\n",
    "    return np.dot(ionpos,np.transpose(M))\n",
    "        \n",
    "def cart2lat(M,ionpos):\n",
    "    return np.dot(ionpos,np.linalg.inv(np.transpose(M)))\n",
    "\n",
    "def make_supercell(M,ionpos,ionspecies,nx,ny,nz):\n",
    "    newM = np.dot( M,np.diag((nx,ny,nz)) )\n",
    "    newIonpos = []\n",
    "    newIonspecies = []\n",
    "    for i in range(nx):\n",
    "        fi = i/nx\n",
    "        for j in range(ny):\n",
    "            fj = j/ny\n",
    "            for k in range(nz):\n",
    "                fk = k/nz\n",
    "                for v in ionpos:\n",
    "                    newIonpos.append([(i+v[0])/nx,(j+v[1])/ny,(k+v[2])/nz])\n",
    "                newIonspecies = np.concatenate((newIonspecies,ionspecies))\n",
    "    return newM,np.array(newIonpos),newIonspecies\n",
    "\n",
    "# Read in the geometry of the flake and the substrate\n",
    "flakeIonpos_uc, flakeIonsp_uc, flakeLattice_uc, subsIonpos_uc, subsIonsp_uc, subsLattice_uc = input_options[inputParams[\"INPUT_FORMAT\"][0]]()\n",
    "       \n",
    "# Ensure that the flake and substract fractional ion positions are compact i.e. -0.5 < z < 0.5\n",
    "# This ensures transformation to cartesian coordinates works properly\n",
    "flakeIonpos_uc = np.array(flakeIonpos_uc)\n",
    "subsIonpos_uc = np.array(subsIonpos_uc)\n",
    "flakeIonpos_uc[:,2] = np.mod(flakeIonpos_uc[:,2] + 0.5, 1.0) - 0.5\n",
    "subsIonpos_uc[:,2]  = np.mod(subsIonpos_uc[:,2] + 0.5,  1.0) - 0.5\n",
    "    \n",
    "#print(flakeIonpos_uc)\n",
    "#print(flakeIonsp_uc)\n",
    "#print(flakeLattice_uc)\n",
    "\n",
    "#print(subsIonpos_uc)\n",
    "#print(subsIonsp_uc)\n",
    "#print(subsLattice_uc)\n",
    "\n",
    "# Edit ionsp to add unique identity to each of the atoms from the unitcell (e.g. C1, C2, ...)\n",
    "# This will let me identify their unique nieghborhoods when doing flake termination\n",
    "cur_sp = flakeIonsp_uc[0]\n",
    "count = 0\n",
    "for i in range(len(flakeIonsp_uc)):\n",
    "    sp = flakeIonsp_uc[i]\n",
    "    if sp != cur_sp:\n",
    "        cur_sp = sp\n",
    "        count = 0\n",
    "    flakeIonsp_uc[i] += str(count)\n",
    "    count += 1\n",
    "\n",
    "#print(flakeIonsp_uc)\n",
    "    \n",
    "\n",
    "# For each atom in the flake's unit cell\n",
    "my_tuple_list = []\n",
    "for i in range(len(flakeIonpos_uc)):\n",
    "    sc_lat,sc_ip,sc_is = make_supercell(flakeLattice_uc,flakeIonpos_uc,flakeIonsp_uc,3,3,1)\n",
    "    centerCell_ip = flakeIonpos_uc + np.array([1,1,0])\n",
    "    \n",
    "    # convert to cartesian coords\n",
    "    sc_ip = lat2cart(sc_lat,sc_ip)\n",
    "    centerCell_ip = lat2cart(flakeLattice_uc,centerCell_ip)\n",
    "    \n",
    "    # compute displacements from atom i to all other atoms\n",
    "    # sort from smallest to largest\n",
    "    disps = sc_ip - centerCell_ip[i]\n",
    "    dists = np.linalg.norm(disps,axis=1)\n",
    "    sort_indecies = np.argsort(dists)\n",
    "    disps = disps[sort_indecies]\n",
    "    dists = dists[sort_indecies]\n",
    "    \n",
    "    # Find the displacements to the 1NNs\n",
    "    NN_cart_disps = [disps[1]] # skip the zero displacement to itself\n",
    "    for j in range(2,len(disps)):\n",
    "        if abs(dists[j]-dists[j-1]) < 0.5:  # noise tolerance of 0.5 bohr\n",
    "            NN_cart_disps.append(disps[j])\n",
    "        else:\n",
    "            break\n",
    "            \n",
    "    # Add a new tuple to the key, values list\n",
    "    my_tuple_list.append((flakeIonsp_uc[i],NN_cart_disps))\n",
    "\n",
    "# Create a dictionary where keys are unique unitcell ion labels, values are the displacements to its 1NNs \n",
    "NN_dict = dict(my_tuple_list)\n",
    "    \n",
    "#print(NN_dict)\n",
    "\n",
    "# Create Flake supercell \n",
    "# Handle the possibility of fractional supercells by generating a ceil(nx) x ceil(ny) supercell\n",
    "# And adding two additional cuts\n",
    "nx = float(inputParams[\"FlakeSupercell\"][0])\n",
    "ny = float(inputParams[\"FlakeSupercell\"][1])\n",
    "print(\"Flake Supercell:\", nx,ny)\n",
    "flakeLattice,flakeIonpos,flakeIonsp = make_supercell(flakeLattice_uc,flakeIonpos_uc,flakeIonsp_uc,int(np.ceil(nx)),int(np.ceil(ny)),1) \n",
    "Ra_cart = lat2cart(flakeLattice_uc,np.array([nx,0,0]))\n",
    "Rb_cart = lat2cart(flakeLattice_uc,np.array([0,ny,0]))\n",
    "inputParams[\"FlakeCut\"].append([Ra_cart[0],Ra_cart[1],-flakeLattice_uc[1][1], flakeLattice_uc[0][1],\"cartesian\"]) # R+90\n",
    "inputParams[\"FlakeCut\"].append([Rb_cart[0],Rb_cart[1], flakeLattice_uc[1][0],-flakeLattice_uc[0][0],\"cartesian\"]) # R-90\n",
    "\n",
    "# Convert to Cartesian\n",
    "flakeIonpos = lat2cart(flakeLattice,flakeIonpos)\n",
    "\n",
    "plt.title(\"Flake Before cuts\")\n",
    "plt.scatter(flakeIonpos[:,0],flakeIonpos[:,1])\n",
    "plt.show()\n",
    "\n",
    "# Perform Cuts on the Flake\n",
    "for cut in inputParams[\"FlakeCut\"]:\n",
    "    print(\"cut =\", cut)\n",
    "    center = np.array([float(cut[0]),float(cut[1]),0.0])\n",
    "    normal = np.array([float(cut[2]),float(cut[3]),0.0])\n",
    "    \n",
    "    # convert into cartesian coordinates if given in flake lattice coordinates\n",
    "    if cut[4] == \"Angstrom\":\n",
    "        center /= Angstrom\n",
    "        normal /= Angstrom\n",
    "    elif cut[4] == \"latticeFlake\":\n",
    "        center = lat2cart(flakeLattice_uc,center)\n",
    "        normal = lat2cart(flakeLattice_uc,normal)\n",
    "    \n",
    "    # toss all the ions on the negative side of the cut\n",
    "    delList = []\n",
    "    for i in range(len(flakeIonpos)):\n",
    "        #print(\"flakeIonpos[i]-center\", flakeIonpos[i]-center)\n",
    "        #print(\"normal\",normal)\n",
    "        if np.dot(flakeIonpos[i]-center, normal) <= 0:\n",
    "            delList.append(i)\n",
    "            #print(\"removing this ion!\")\n",
    "    flakeIonpos = np.delete(flakeIonpos,delList,axis=0)\n",
    "    flakeIonsp = np.delete(flakeIonsp,delList)\n",
    "    \n",
    "plt.title(\"Flake After Cuts\")\n",
    "plt.scatter(flakeIonpos[:,0],flakeIonpos[:,1])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Now I have a trimmed flake in cartesian coordinates\n",
    "# Terminate the edges of the flake with placeholder atoms to fill up 1NN sites of the edge atoms\n",
    "#print(\"*inputParams[FlakeTermination]\", *inputParams[\"FlakeTermination\"])\n",
    "if inputParams[\"FlakeTermination\"][0] == 'Y':\n",
    "    terminators = np.array([[]]) # Termination sites\n",
    "    coordnums = np.array([]) # Coordination numbers of termination sites\n",
    "    dispsFromFlake = []\n",
    "    for i in range(len(flakeIonpos)):\n",
    "        # Identify its desired 1NN sites\n",
    "        NN_disps = NN_dict[ flakeIonsp[i] ] \n",
    "        NN_sites = NN_disps + flakeIonpos[i]\n",
    "\n",
    "        # If any of its desired 1NN sites are not occupied, and not already in the list of flake terminators, add the site\n",
    "        for j in range(len(NN_sites)): \n",
    "            site = NN_sites[j]\n",
    "            # consider a site filled if it is occupied within a tolerance of 0.1 bohr\n",
    "            if np.min( np.linalg.norm(flakeIonpos - site, axis=1) ) >= 0.1 :\n",
    "                # Just add this site if no other termination sites identified\n",
    "                if terminators.shape[1] == 0:\n",
    "                    terminators = np.append(terminators,[site],axis=1)\n",
    "                    coordnums = np.append(coordnums,[1])\n",
    "                    dispsFromFlake.append([NN_disps[j]]) # store displacement vector from flake to this site\n",
    "                # Add if this site not shared by a previous atom\n",
    "                elif np.min( np.linalg.norm(terminators - site, axis=1) ) >= 0.1 :\n",
    "                    terminators = np.append(terminators,[site],axis=0)\n",
    "                    coordnums = np.append(coordnums,[1])\n",
    "                    dispsFromFlake.append([NN_disps[j]]) # store displacement vector from flake to this site\n",
    "                # Else increment the coordination number of this termination site\n",
    "                else:\n",
    "                    indx = np.argmin( np.linalg.norm(terminators - site, axis=1) )\n",
    "                    coordnums[indx] += 1 \n",
    "                    dispsFromFlake[indx].append(NN_disps[j])\n",
    "\n",
    "    #print(\"Terminating Sites:\")\n",
    "    #print(terminators)\n",
    "    #print(\"Coordination Numbers:\")\n",
    "    #print(coordnums)\n",
    "    #print(\"Displacements from the flake:\")\n",
    "    #print(dispsFromFlake)\n",
    "    \n",
    "    # It is neccessary to reduce the distance of the coordnum=1 terminating sites if they are a small atom (e.g. H)\n",
    "    # Otherwise they may not bond to or stabilize the flake edge\n",
    "    for i in range(len(coordnums)):\n",
    "        if coordnums[i] == 1 and float(inputParams[\"TermAtomDist\"][0]) != 0:\n",
    "            newdisp = (dispsFromFlake[i][0] / np.linalg.norm(dispsFromFlake[i][0]) ) * float(inputParams[\"TermAtomDist\"][0])\n",
    "            if inputParams[\"TermAtomDist\"][1] == \"Angstrom\":\n",
    "                newdisp /= Angstrom\n",
    "            terminators[i] -= dispsFromFlake[i][0]\n",
    "            terminators[i] += newdisp\n",
    "                    \n",
    "    # Add terminating atoms to the MINT flake\n",
    "    flakeIonpos = np.concatenate((flakeIonpos,terminators))\n",
    "    flakeIonsp = np.concatenate((flakeIonsp,['H']*len(terminators)))\n",
    "\n",
    "    \n",
    "    \n",
    "# Remove unique ionsp labels from the flake\n",
    "for i in range(len(flakeIonsp)):\n",
    "    flakeIonsp[i] = ''.join([j for j in flakeIonsp[i] if not j.isdigit()])\n",
    "\n",
    "plt.title(\"Flake After Termination\")\n",
    "plt.scatter(flakeIonpos[:,0],flakeIonpos[:,1])\n",
    "plt.show()\n",
    "\n",
    "# Rotate the flake about the specified axis\n",
    "rotateParams = inputParams[\"FlakeRotate\"] #\"FlakeRotate\": [\"0.0\", \"rad\", \"0.0\", \"0.0\", \"cartesian\"]\n",
    "#print(rotateParams)\n",
    "if rotateParams[1] == \"rad\":\n",
    "    theta = float(rotateParams[0])\n",
    "elif rotateParams[1] == \"deg\":\n",
    "    theta = float(rotateParams[0]) * np.pi/180\n",
    "else:\n",
    "    print(\"FlakeRotate needs to be [theta] [deg/rad] [x] [y] [cartesian/latticeFlake]\")\n",
    "    theta = float(rotateParams[0])\n",
    "\n",
    "center = np.array([float(rotateParams[2]),float(rotateParams[3]),0.0])\n",
    "if rotateParams[4] == \"Angstrom\":\n",
    "    center /= Angstrom \n",
    "elif rotateParams[4] == \"latticeFlake\":\n",
    "    center = lat2cart(flakeLattice_uc,center)\n",
    "    \n",
    "# Shift flake so that the rotation center lies at the origin\n",
    "flakeIonpos -= center \n",
    "# Rotate the flake in the centered coordinate system\n",
    "R = np.array([[np.cos(theta),-np.sin(theta),0.],\n",
    "              [np.sin(theta), np.cos(theta),0.],\n",
    "              [           0.,            0.,1.]])\n",
    "flakeIonpos = np.dot(R,flakeIonpos.T).T\n",
    "# Shift back to original location\n",
    "flakeIonpos += center\n",
    "\n",
    "plt.title(\"Flake After Rotation\")\n",
    "plt.scatter(flakeIonpos[:,0],flakeIonpos[:,1])\n",
    "plt.show()\n",
    "\n",
    "# Shift the flake by the prescribed ammount\n",
    "shiftParams = inputParams[\"FlakeShift\"] # \"FlakeShift\": [\"0.0\", \"0.0\", \"cartesian\"]\n",
    "shift = np.array([float(shiftParams[0]),float(shiftParams[1]),0.0])\n",
    "if shiftParams == \"Angstrom\":\n",
    "    shift /= Angstrom \n",
    "elif shiftParams == \"latticeFlake\":\n",
    "    shift = lat2cart(shift,flakeLattice_uc)\n",
    "elif shiftParams == \"latticeSubstrate\":\n",
    "    shift = lat2cart(shift,subsLattice_uc)\n",
    "\n",
    "flakeIonpos += shift\n",
    "    \n",
    "plt.title(\"Flake After Planar Shift\")\n",
    "plt.scatter(flakeIonpos[:,0],flakeIonpos[:,1])\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"Flake After Z Shift\")\n",
    "plt.scatter(flakeIonpos[:,0],flakeIonpos[:,2])\n",
    "plt.show()\n",
    "    \n",
    "    \n",
    "# Generate the smallest substrate supercell that fits the flake\n",
    "\n",
    "# Naive but easy to code\n",
    "def getMinDist(setA,setB):\n",
    "    retval = np.infty\n",
    "    for a in setA:\n",
    "        for b in setB:\n",
    "            d = np.linalg.norm(a-b)\n",
    "            if d < retval:\n",
    "                retval = d\n",
    "    return retval\n",
    "\n",
    "pad_a1 = float(inputParams[\"MinVacuumPad\"][0])\n",
    "pad_a2 = float(inputParams[\"MinVacuumPad\"][1])\n",
    "\n",
    "if inputParams[\"MinVacuumPad\"][2] == \"Angstrom\":\n",
    "    pad_a1 /= Angstrom\n",
    "    pad_a2 /= Angstrom\n",
    "\n",
    "# Substrate Unitcell Lattice Vectors\n",
    "a1 = subsLattice_uc[:,0] #np.array([1.5*a, SQRT3_2*a,0.0])\n",
    "a2 = subsLattice_uc[:,1] #np.array([1.5*a,-SQRT3_2*a,0.0])\n",
    "\n",
    "maxiter = 100\n",
    "m_a1 = 1\n",
    "for m in range(1,maxiter):\n",
    "    d = getMinDist(flakeIonpos,flakeIonpos + m*a1)\n",
    "    if d >= pad_a1: \n",
    "        m_a1 = m\n",
    "        break\n",
    "\n",
    "n_a2 = 1\n",
    "for n in range(1,maxiter):\n",
    "    d = getMinDist(flakeIonpos,flakeIonpos + n*a2)\n",
    "    if d >= pad_a2: # 5 bohr\n",
    "        n_a2 = n\n",
    "        break\n",
    "\n",
    "print(\"Making a\",m_a1, \"x\",n_a2, \"supercell of the substrate\")\n",
    "\n",
    "# Now construct the supercell\n",
    "subsLattice, subsIonpos, subsIonsp = make_supercell(subsLattice_uc,subsIonpos_uc,subsIonsp_uc,m_a1,n_a2,1) # returns in lattice coords\n",
    "subsIonpos = lat2cart(subsLattice,subsIonpos)\n",
    "\n",
    "plt.title(\"Substrate\")\n",
    "plt.scatter(subsIonpos[:,0],subsIonpos[:,1])\n",
    "plt.show()\n",
    "\n",
    "#print(\"SubsIonpos:\")\n",
    "#print(subsIonpos)\n",
    "\n",
    "# Finally it's time to place the flake on top of the substrate\n",
    "\n",
    "#find the z-coord on the top most substrate atom. This is the top of the substrate\n",
    "subsTop = np.max(subsIonpos[:,2])\n",
    "\n",
    "#find the z-coord on the bottom most flake atom. This is the bottom of the flake\n",
    "flakeBot = np.min(flakeIonpos[:,2])\n",
    "\n",
    "#shift the flake in the z-dir so that it lies on top of the substrate separated by the indicated distance\n",
    "il_dist = float( inputParams[\"InterlayerDistance\"][0] )\n",
    "if inputParams[\"InterlayerDistance\"][1] == \"Angstrom\":\n",
    "    il_dist /= Angstrom\n",
    "flakeIonpos += np.array([0.0,0.0,il_dist-(flakeBot-subsTop)])\n",
    "\n",
    "# Combine the flake and substrate\n",
    "ionpos = np.concatenate((flakeIonpos,subsIonpos))\n",
    "ionsp = np.concatenate((flakeIonsp,subsIonsp))\n",
    "# Make a list of colors for debug plot visualization:\n",
    "colors = len(flakeIonpos)*['r'] + len(subsIonpos)*['b']\n",
    "\n",
    "plt.title(\"MINT Proxy System Top View\")\n",
    "plt.scatter(ionpos[:,0],ionpos[:,1], c=colors)\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"MINT Proxy System Side View\")\n",
    "plt.scatter(ionpos[:,0],ionpos[:,2], c=colors)\n",
    "plt.show()\n",
    "\n",
    "plt.title(\"MINT Proxy System Side View\")\n",
    "plt.scatter(ionpos[:,1],ionpos[:,2], c=colors)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# The out of plane lattice vector is specified by the user\n",
    "# This allows for the possibily of different interlayer stacking\n",
    "lattice = subsLattice\n",
    "c_vec = np.array([float(inputParams[\"LatticeVectorC\"][0]),float(inputParams[\"LatticeVectorC\"][1]),float(inputParams[\"LatticeVectorC\"][2])])\n",
    "if inputParams[\"LatticeVectorC\"][3] == \"Angstrom\":\n",
    "    c_vec /= Angstrom\n",
    "lattice[:,2] = c_vec\n",
    "\n",
    "# Finally, convert back into lattice coordinates\n",
    "ionpos = cart2lat(lattice,ionpos)\n",
    "\n",
    "# Now sort by ionspecies type\n",
    "# TODO: maybe sort each ionspecies by their z-coordinate or distance from the x-y plane\n",
    "#       this would allow post-processing scripts to more easily separate the flake from the substrate\n",
    "sort_inds = np.argsort(ionsp)\n",
    "ionsp = ionsp[sort_inds]\n",
    "ionpos = ionpos[sort_inds]\n",
    "\n",
    "# Write structure files based on the chosen DFTCODE_OUTPUT\n",
    "path = \".\"\n",
    "output_options[inputParams[\"OUTPUT_FORMAT\"][0]](lattice,ionpos,ionsp,path)\n",
    "#output_options[x](lattice,ionpos,ionsp,path)\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1d45cb0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
